文献类型：期刊文献

中文题名：基于深度学习的多用户MIMO-OFDM-IM系统符号检测技术

英文题名：DEEP LEARNING-BASED SYMBOL DETECTION TECHNOLOGY FORMULTI-USER OF MIMO-OFDM-IM SYSTEM

作者：马雪娇[1];袁伟娜[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2024

卷号：41

期号：1

起止页码：78

中文期刊名：计算机应用与软件

外文期刊名：Computer Applications and Software

收录：CSTPCD;;北大核心:【北大核心2023】；

语种：中文

中文关键词：MIMO-OFDM;索引调制;DNN;符号检测

外文关键词：MIMO-OFDM;Index modulation;DNN;Symbol detection

摘要：MIMO-OFDM-IM是将MIMO-OFDM(多输入多输出正交频分复用)和IM(索引调制)结合的一种新颖的多载波调制技术。为解决目前多用户MIMO-OFDM索引调制的符号检测技术仍存在的复杂度高和误码性能受用户数量影响严重的问题,提出一种基于深度学习的符号检测框架,在该框架中,编码器与解码器都由DNN(深度神经网络)构造,采用监督式学习训练数据。实验结果表明,该框架有效地解决了以上问题。
MIMO-OFDM-IM is a novel multicarrier modulation technique combining MIMO-OFDM(multiple-input multiple-output orthogonal frequency division multiplexing)and IM(index modulation).In order to solve the problem that the symbol detection technology of multi-user MIMO-OFDM index modulation still has high complexity and the bit error performance is seriously affected by the number of users,a symbol detection framework based on deep learning is proposed.Both encoders and decoders were constructed by DNN(deep neural network),using supervised learning training data.The experimental results show that the above problems are effectively solved.